Large-scale mRNA analysis of female skeletal muscles during 60 days of bed rest with and without exercise or dietary protein supplementation as countermeasures
Bibliographic record
Abstract
Microgravity has a dramatic impact on human physiology, illustrated in particular, with skeletal muscle impairment. A thorough understanding of the mechanisms leading to loss of muscle mass and structural disorders is necessary for defining efficient clinical and spaceflight countermeasures. We investigated the effects of long-term bed rest on the transcriptome of soleus (SOL) and vastus lateralis (VL) muscles in healthy women (BRC group, n = 8), and the potential beneficial impact of protein supplementation (BRN group, n = 8) and of a combined resistance and aerobic training (BRE group, n = 8). Gene expression profiles were obtained using a customized microarray containing 6,681 muscles-relevant genes. A two-class statistical analysis was applied on 2,103 genes with consolidated expression in BRC, BRN, and BRE groups. We identified 472 and 207 mRNAs whose expression was modified in SOL and VL from BRC group, respectively. Further clustering analysis, identifying relevant biological mechanisms and pathways, reported five main subclusters. Three are composed of upregulated mRNAs involved mainly in nucleic acid and protein metabolism, and two made up of downregulated transcripts encoding components involved in energy metabolism. Exercise countermeasure demonstrated drastic compensatory effects, decreasing the number of differentially expressed mRNAs by 89 and 96% in SOL and VL, respectively. In contrast, nutrition countermeasure had moderate effects and decreased the number of differentially-expressed transcripts by 40 and 25% in SOL and VL. Together, these data present a systematic, global and comprehensive view of the adaptive response of female muscle to long-term atrophy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".